Journal of Water Resources Planning and Management

At the 2013 World Environmental and Water Resources Congress in Cincinnati, Ohio, the ASCE Task Committee on Research Databases for Water Distribution Systems was formed to develop an online open-access repository of water distribution system hydraulic network files for use in applied scientific research. This paper discusses the development of the resulting database, specific model contributions, available model building toolkits, general methods for system classification, and general information related to the database platform and content. It is hoped that the assembled database will promote the advancement of water distribution research into the next decade.

Urban water system optimization, e.g., chlorine dosage optimization, requires repeatedly running hydraulic and water quality models, which leads to significant computational and time costs for large-scale real networks. In the process of optimization, the search space of decision variables is adjusted downward to obtain the lowest-cost scheduling plans, which lead to a large number of negative samples and low optimization efficiency. To address this problem, this study proposes an efficient constraint-based pruning method (CBPM) that uses accumulated data during the optimization calculation to determine whether a sample meets the constraints before running simulation models, thereby pruning negative samples and improving optimization efficiency. An example network and a large-scale real network were used as case studies to demonstrate the performance of the proposed CBPM. The results show that the proposed CBPM significantly can improve optimization results using the same calculations as the simulation model. For example, application in a real water distribution network showed that to obtain the same total chlorine dosage solutions, the model using the proposed CBPM saved 57.9% of the calculations compared with the original optimization model.

This paper proposes a new method to identify the near-optimal response to failures in water distribution networks in near real time. The response method is formulated as a two-objective optimization problem with objectives being the minimization of failure impacts and related operational costs. The new heuristics-based method is developed and used to solve this optimization problem. The method comprises three steps. In the first step, the initial list of available interventions is identified offline. In the second step (online), the narrowed-down list of interventions considered in the optimization is identified. Finally, in the last step (online), a novel heuristic algorithm is applied to identify near-optimal solutions in near real time. The new optimization method was validated and demonstrated in two case studies, a semireal case study based on a C-Town network and an assumed failure event (pipe burst), and a real UK case study involving a complex real pipe network and event caused by shutting down the Water Treatment Works. The Pareto front of response interventions identified by the new heuristics method approximates well the non-dominated sorting genetic algorithm II Pareto front in both cases with the largest differences measured in terms of end-impacts (between relevant solutions for the same cost) being 4% and 9%, respectively. In addition, the new heuristics method is able to identify near-optimal response solutions in a computationally fast manner (15 min and 1 h for the two cases). Therefore, the heuristics method can be used in near real time in real-life situations.

The formation of district metered areas (DMAs) is an efficient strategy for the operation and management of water distribution networks (WDNs). Identifying the most suitable DMA layout is a challenging task for water utilities as it may involve several aspects that need to be addressed simultaneously. This study presents a novel multiphase approach for optimal DMA design that involves: (1) a combination of a fast Newman algorithm (FNA) to identify initial clusters; (2) a nondominated sorting genetic algorithm (NSGA-III) to obtain a set of good DMA configurations while considering several objectives simultaneously; and (3) a multiple attribute decision-making method (MADM) to find the best suited DMA configuration from a set of feasible alternative solutions based on the preference given to each objective. The proposed methodology is applied to two networks including a large benchmark network and a real-life water network. Four problem objectives out of several possible objectives were considered. These are: (1) the total cost of implementation (economic criterion); (2) the pressure deviation (hydraulic criterion); (3) a resilience index (energy criterion); and (4) the total demand shortfall (customer satisfaction criterion). Finally, a multiple attribute decision-making tool [i.e., a simple additive weighing (SAW) method] was used to arrive at a unique solution out of a set of feasible solutions. Results show that the proposed methodology can effectively identify DMAs while considering multiple objectives.

Excessive hydraulic transients in a water distribution system (WDS) can instantaneously damage equipment and infrastructure, while long-term pressure oscillations can contribute to pipe structural deterioration and eventually pipe bursts. It is therefore important to monitor hydraulic transient events in WDSs and locate the source in a timely manner; however, there is a lack of a theoretical basis for optimal sensor placement with regard to real-time transient event detection. This paper investigates the criteria for optimal sensor deployment of high-speed pressure loggers in a WDS and develops a technique for determining the optimal sensor locations. The proposed criteria focus on maximizing the spatial extent of the network within which transient events can be detected and located. A key concept in this work is the locatability of an event, which is defined based on the combination of hydraulic wave propagation theory in networks and the adoption of graphing theoretic concepts, and is based on the existence of unique wave propagation paths from the event to two or more sensors. Two case studies are considered, where the first is a small network that is used to explain the steps of the method, and the second serves as the basis for an extensive numerical study, where it is observed that the proposed method outperforms other approaches and is able to provide the optimum sensor layout for a given number of sensors.

Contamination events in water distribution systems (WDS) are emergencies that cause public health crises and require fast response by the responsible utility manager. Various models have been developed to explore the reactions of relevant stakeholders during a contamination event, including agent-based modeling. As the COVID-19 pandemic has changed the daily habits of communities around the globe, consumer water demands have changed dramatically. In this study, an agent-based modeling framework is developed to explore social dynamics and reactions of water consumers and a utility manager to a contamination event, while considering regular and pandemic demand scenarios. Utility manager agents use graph theory algorithms to place mobile sensor equipment and divide the network in sections that are endangered of being contaminated or cleared again for water consumption. The status of respective network nodes is communicated to consumer agents in real time, and consumer agents adjust their water demands accordingly. This sociotechnological framework is presented using the overview, design, and details protocol. The results comprise comparisons of reactions and demand adjustments of consumers to a water event during normal and pandemic times, while exploring new methods to predict the fate of a contaminant plume in the WDS.

The presence of leaks in water distribution networks (WDN) is a problem of global concern that causes significant annual losses of treated water. Most efforts to solve this problem concentrate on the development of methods to help locate leaks in the least possible time. However, the decision to repair leaks is not always trivial because of their uncertain location, size, and other related consequences. For example, intervening places of difficult access or those that would generate major city disruptions may trigger the decision of not taking any action. This paper presents a methodology to determine the optimal location of pressure sensors for leak detection considering the concept of value of information (VOI) using a model-based optimization framework. The methodology allows for estimating the potential value of the data provided by a sensor in terms of its usefulness to assist decisions. The methodology, which also describes how to interpret VOI results, was applied to one of the water districts of a city in Western Europe. Results showed that the optimal VOI-based sensor placement outperformed the existing pressure sensor network for leak detection.

The potential utility of forecast information to improve reservoir control is well established, but physical, operational, and institutional constraints inhibit the full realization of these benefits. To help integrate forecast technology into operational water management, it is necessary to establish the opportunities and limitations of forecast use within the current system management framework, so that water managers and planners can identify in practice what constraints or aspects of control policy design most limit forecast utility. In this work, we explore the value of forecasts in the current outflow management plan of the Lake Ontario-St. Lawrence River basin, which is the first regulation policy of this system to use water supply forecasts to guide release decisions. We assess the value of forecasts for many-objective system management (e.g., upstream and downstream flood risk reduction, hydropower production, commercial navigation costs, recreational boating benefits, and wetland restoration), as a function of forecast accuracy, timing, and interactive effects, with system constraints and control rule structures. Results show that a 20%–30% reduction in forecast error over current statistical forecasts leads to observable improvements in most objectives, which grow significantly with additional forecast accuracy. Realizing these benefits is possible by increasing forecast accuracy, particularly in the springtime when tradeoffs between objectives are the greatest. However, we also identify one objective (wetland restoration) that declines with improved forecast skill, as well as operational constraints that limit forecast utility under the current outflow management plan. Overall, these results suggest that key aspects of the current control policy could be revised to better utilize forecast information, especially as forecasts improve over time.

North America is facing an aging watermain infrastructure crisis. Pipes are surpassing their expected service life, and surveys continue to suggest that the rate of pipe breaks is increasing dramatically. This paper investigates two utilities in Ontario, Canada, that have managed to halt, and even decrease, break rates substantially over the last two decades. Specifically, the impact of adopting early rehabilitation for both cities, including hot-spot cathodic protection, retrofit cathodic protection, lining, early adoption of PVC, and proactive pipe replacement are examined. A random survival forest model is developed for major pipe cohorts for both cities, and using this machine-learning survival analysis model, the numbers of breaks avoided due to pipe rehabilitation are examined. The results indicate pipe rehabilitation techniques avoided 14,572 breaks between 1986 and 2015 for City A and 764 breaks between 1990 and 2020 for City B, and, in the absence of these undertakings, incurred break rates would have been more than 50% greater for both cities. Therefore, pipe rehabilitation is a major factor that has resulted in the declining break rates identified in both cities, suggesting that other cities struggling with increasing break rates should consider similar rehabilitation strategies.